• My origins in programming, data science, and open science

• Improving reproducibility, collaboration and communication in environmental science with open science tools

• Resources and recommendations

Reproducibility is foundatational to science, but we rarely test it, even with our own work.

Fig of headlines (does this slide fit here)

Data Science:

"an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge" (Grolemund & Wickham 2016)

Data Science:

"an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge" (Grolemund & Wickham 2016)

Data Science:

"an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge" (Grolemund & Wickham 2016)

Open Science:

"the concept of transparency at all stages of the research process, coupled with free and open access to data, code, and papers" (Hampton et al. 2014)

I primarily use these tools:

• R
• RStudio
• git
• GitHub

TODO: images

This talk: jules32.github.io/opensci-talk

• what are Humboldt squid habitat preferences?
• what season are they most abundant?
• how fast and far can they migrate?
• how do they interact with other species?
• how do I import my data when it's too big for Excel?
• how do I subset years or other attributes?
• how on earth do I visualize any of this?

• in a panic
• for a single purpose (get this thesis done!)
• in near-isolation*
  
  
  

• in a panic
• for a single purpose (get this thesis done!)
  
• in near-isolation*
  
  
  

* Thankfully, I had wonderful programming mentors:

Steve Haddock, Dave Foley, Ashley Booth

TODO: image

• method to categorize benefits that oceans provide to people
• scores are modeled using existing data
• method can be tailored to different geographies
• can help inform policy decisions, especially when repeated

2012: OHI method and first global assessment (Halpern et al. 2012)

2013: second annual global assessment

• repeat methods
• update data
• compare between years

2013: second annual global assessment

• repeat methods
• update data
• compare between years
  
  
  

We expected to easily reproduce our previous work. We had planned ahead:

• coded models
• 130 pages of published supplemental material
• internal documents and notes

We struggled to reproduce our work using standard approaches to reproducibility and collaboration

• added challenge of managing multiple years of information and scores
• we needed a nimble approach to sharing data, methods, and results within and outside our team

Lowndes et al. in prep: Improving reproducibility, collaboration, and communication in environmental science using open science tools

underscore the importance of:

• exposure and confidence
• evolution rather than revolution

1. reproducibility, including transparency and repeatability, particularly in data preparation
2. collaboration, including teamwork and internal collaboration
3. communication with scientific and public communities

"Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing data, before it can be explored for useful information." - NYTimes (2014)

• transforming, rescaling, gap-filling, formatting, etc
• seldom mentioned but underpins the scientific process

"Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing data, before it can be explored for useful information." - NYTimes (2014)

• transforming, rescaling, gap-filling, formatting, etc
• seldom mentioned but underpins the scientific process

Ultimate goal: Tidy data (Wickham 2014)

Before

• manually (without coding)
• largely Microsoft Excel
• internal documents and emails

After

• full process coded
  • R with documentation
  • RMarkdown

Data wrangling with the grammar of data manipulation and tidy data
Grolemund & Wickham 2016: R for Data Science

TODO: version control quote

Before

• filenames suffixed with dates, initials (e.g. final.csv and final_JL-2016-08-05.csv)
• email descriptions of what changed between files

After

• version control with git
• short messages accompany commited changes

TODO: Collaboration quote

Before

• team structure: scientists and single programmer
• discrete tasks:
  • scientists developed the models conceptually, gathered data, prepared data, interpreted modeled results
  • programmer coded the models.
• communication + file sharing: email chains (often forwarded).
• organization: individual conventions on local computers

After

• team structure: data scientists
• workflow: (simplified) GitHub workflow

Before

• published manuscripts
• data on FTP server

After

• published manuscripts
• work openly on GitHub github.com/OHI-Science
• website with methods, instruction, data OHI-Science.org

• incremental
• always improving, learning
• teaching and training, support

These tools and this workflow make our work possible.

• December 8 2016: releasing 5th global assessment
• Support and training for ~26 government or academic 'OHI+' assessments

All on ohi-science.org

1 - Learn to code

    - in R
    - with RStudio

2 - Use version control

    - git
    - with GitHub
    - through RStudio

Introduce these concepts incrementally: evolution not revolution

Books, trainings, and webinars that helped me:

• ohi-science.org/news: Resources for R and Data Science

Recent academic publications:

• "How open science helps researchers succeed" McKiernan et al. 2016
• "Best Practices for Scientific Computing" Wilson et al. 2014
• "Good Enough Practices for Scientific Computing" Wilson et al. 2016

THANK YOU

email: lowndes @nceas.ucsb.edu
twitter: @juliesquid
talk url: https://jules32.github.io/opensci-talk

15-minute version of this talk at WSN: Friday, Nov 11, Session 7, 3pm
NCEAS is hiring: www.nceas.ucsb.edu/positionsopen

–> TODO: quote from Woo et al

• R and RStudio for coding and visualization
• Git for version control, GitHub for collaboration
• GitHub + RStudio for organization, documentation, online publishing, distribution, and communication ohi-science.org

reproducibility

• data preparation: coding and documenting
• modeling: R functions and packages
• version control
• organization

collaboration

• teamwork
• (simplified) GitHub workflow

communication

• sharing data and code
• sharing methods and instruction

• as a grad student I learned due to panic
• just needed to get the job done
• PCfB
• resistant to version control